基于仓室SIR模型与Gauss函数的新型冠状病毒肺炎(COVID-19)疫情分析

Analysis of COVID-19 based on SIR model and Gaussian function

本研究对传统仓室SIR模型进行改进,引入Gauss函数模拟COVID-19疫情的日传染率、日隔离率变化情况。采用Runge-Kutta算法有效拟合了湖北地区的疫情发展情况,依此推测出COVID-19的基本再生数R0=2.4817,有效再生数Re=0.9912,验证了COVID-19的高传染性。拟合结果证明,Gauss函数极为有效地反映感染率、确诊率的实际变化情况。因此,通过数学模型分析疫情关键参数对防控具有指导意义。

We improved the SIR model of traditional warehouse and introduced Gaussian function to simulate the daily infection rate and diagnosis rate of COVID-19.The Runge-Kutta algorithm was used to fit the epidemic situation in Hubei.The basic regeneration number of COVID-19 was induced as R0=2.4817 and the effective regeneration number Re=0.9912,which verified the high infectivity of COVID-19.The fitting results showed that Gauss function was very effective in reflecting the actual changes of infection rate and diagnosis rate.So it is of great referential importance for the current epidemic prevention through mathematical model to analyze the key parameters of epidemic situation.